Methodological challenges in outcomes research for early-trials for implementation of new therapies in neuropediatric rare diseases
Notice bibliographique
Résumé
During 2023, the leads of the ECNP TWG focused on Clinical Outcomes in Early-Trials in Neurosciences scheduled two brainstorming sessions to allow a deep discussion about challenges facing neurosciences drug development in neuropediatric rare diseases. Sessions were led by Dr Maria T. Acosta from the Undiagnosed Disease Program (UDP) at the National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, EE.UU. The experts discussed about challenges and potential solutions as well as alternative options to design appropriated for clinical outcomes assessment (COA) instruments for this population. An important discussion took place due to extensive expertise of the participants and hands-on experience with clinical trials. Participation from experts from several disciplines was a key factor for appraising and brainstorm innovative solutions in the field. Identification of challenges and propose of innovative solutions were a central core of the discussion. Despite of the increasing number of rare and ultra-rare diseases being identify by the day, and the significant differences in biology, and clinical presentation, it is clear most of them face similar problems when is time to select the appropriated COAs to test potential interventions, and experts have a limited potential to drive efficient solutions. Some of the commonly identified common problems include: small number of patients affected, disease changes over time, developmental aspects impacting the clinical presentation according with age, individual variability in terms of disease severity between patient, variable window for intervention and in some cases, need to expedite treatment as per the disease progression. All these and other features, require an extensive dialogue and continue communication between preclinical researchers, clinicians, patients and family members, pharma and treatment designers and regulatory agencies in each condition, making this process, expensive, time consuming and very difficult to accomplish. A feasible solution, that may be applicable to several conditions, is to develop a “back bone” structure to approach rare diseases, allowing each “disease team” to tailored assessments and study design, according with the specific features of the condition. We concluded that it is fundamental to establish effective bridges of communication between the different actors implicated in the clinical trial design and execution, sharing experiences, as well as clear understanding of meaningful outcomes not only for researchers, but clinicians, patients and families. Important emphasis is done in the need or careful selection of COAs in each individual condition to be able to obtain more efficient and reliable results in clinical trials in this population. We need to be creative, and there is a need to leave the “comfort zone” of current methodologies and start piloting new methods at clinical settings. A specific research platform was proposed as a solution in the creation, sharing and validation of new assessment instruments, which would be available to clinicians and researchers attending small patient samples distributed over the world.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,827 | 0,863 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,007 | 0,008 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,006 | 0,016 |
| Communication savante | 0,022 | 0,015 |
| Science ouverte | 0,013 | 0,019 |
| Intégrité de la recherche | 0,007 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».